Title :
Nonlinear Analog Circuit Diagnosis Based on Volterra Series and Neural Network
Author_Institution :
Coll. of Electromech. & Automobile Eng., Chongqing Jiaotong Univ., Chongqing, China
Abstract :
The Volterra kernels are the inherent characteristic of the system. This paper researched how to measure Volterra frequency kernels and used the second Volterra frequency kernels as the fault signatures in diagnosis nonlinear analog circuit. The fault dictionary of nonlinear circuits was constructed based on improved Back-Propagation neural network. Experiment result demonstrates that the method of this paper has high diagnose sensitivity and fast fault identification and deducibility.
Keywords :
Volterra series; analogue circuits; backpropagation; electronic engineering computing; fault diagnosis; neural nets; Volterra frequency kernels; Volterra kernels; Volterra series; backpropagation neural network; fault dictionary; fault identification; fault signatures; nonlinear analog circuit diagnosis; Analog circuits; Circuit faults; Dictionaries; Fault diagnosis; Kernel; Neurons; Nonlinear systems;
Conference_Titel :
Wireless Communications Networking and Mobile Computing (WiCOM), 2010 6th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-3708-5
Electronic_ISBN :
978-1-4244-3709-2
DOI :
10.1109/WICOM.2010.5600110